Beliefs about the development of mental life
Supplemental Materials
Kara Weisman, Lucy S. King, & Kathryn L. Humphreys
July 25, 2021
Extended results
Study 1
Exploratory factor analyses (EFAs)
Parallel analysis
## Parallel analysis suggests that the number of factors = 4 and the number of components = 3## Loading required namespace: GPArotation## Factor Analysis using method = minres
## Call: fa(r = d1_all, nfactors = s1_parallel$nfact, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 MR4 h2 u2
## planning 1.01 -0.11 -0.01 -0.04 0.86 0.14
## having_self_control 0.96 -0.02 -0.01 -0.05 0.86 0.14
## thinking_before_they_act 0.96 -0.03 0.03 -0.04 0.85 0.15
## having_goals 0.95 -0.08 0.00 0.02 0.83 0.17
## reasoning_about_things 0.94 0.00 -0.01 -0.05 0.84 0.16
## controlling_their_emotions 0.92 -0.01 -0.02 -0.06 0.79 0.21
## telling_right_from_wrong 0.91 0.00 0.03 0.01 0.86 0.14
## understanding_what_somebody_else_is_thinking 0.90 -0.05 -0.04 0.03 0.78 0.22
## focusing_on_a_goal 0.90 0.02 0.00 -0.02 0.81 0.19
## feeling_guilty 0.89 -0.04 0.06 0.09 0.84 0.16
## feeling_embarrassed 0.83 0.04 0.01 0.09 0.83 0.17
## feeling_pride 0.83 0.07 -0.05 0.09 0.83 0.17
## making_choices 0.69 0.32 -0.04 -0.02 0.82 0.18
## calming_themselves_down 0.68 0.25 -0.01 -0.05 0.70 0.30
## detecting_danger 0.67 0.07 0.10 0.12 0.68 0.32
## feeling_hopeless 0.67 0.01 0.01 0.28 0.72 0.28
## remembering_things 0.52 0.49 0.01 -0.10 0.77 0.23
## imagining_things 0.51 0.42 -0.01 0.03 0.74 0.26
## recognizing_others_emotions 0.50 0.41 -0.04 0.03 0.68 0.32
## feeling_worried 0.42 0.24 0.05 0.35 0.75 0.25
## getting_hurt_feelings 0.41 0.40 0.00 0.19 0.73 0.27
## having_wants_and_desires 0.29 0.26 0.17 0.27 0.60 0.40
## feeling_excited 0.00 0.85 -0.01 0.06 0.76 0.24
## finding_something_funny 0.06 0.84 -0.02 0.00 0.75 0.25
## loving_somebody 0.05 0.83 -0.10 0.09 0.73 0.27
## learning_from_other_people 0.12 0.80 -0.02 0.01 0.76 0.24
## feeling_happy -0.12 0.79 0.16 0.01 0.70 0.30
## feeling_loved -0.05 0.77 0.04 0.07 0.65 0.35
## recognizing_somebody_else 0.06 0.76 0.17 -0.10 0.73 0.27
## getting_pleasure_from_music 0.10 0.62 0.17 0.04 0.66 0.34
## being_afraid_of_somebody 0.02 0.62 0.11 0.26 0.74 0.26
## listening_to_somebody 0.18 0.61 0.14 -0.03 0.64 0.36
## having_thoughts 0.26 0.59 0.10 0.02 0.71 0.29
## feeling_sad 0.00 0.58 0.15 0.24 0.69 0.31
## feeling_safe 0.02 0.54 0.24 0.13 0.63 0.37
## feeling_textures_(for_example,_smooth,_rough) 0.06 0.54 0.36 -0.05 0.66 0.34
## getting_angry 0.09 0.53 0.05 0.34 0.72 0.28
## feeling_pleasure 0.06 0.47 0.28 0.16 0.64 0.36
## being_angry_at_somebody 0.38 0.42 0.00 0.25 0.79 0.21
## feeling_lonely 0.16 0.42 0.10 0.35 0.69 0.31
## feeling_bored 0.32 0.42 0.04 0.27 0.75 0.25
## feeling_confused 0.16 0.41 0.10 0.36 0.71 0.29
## feeling_scared -0.06 0.41 0.37 0.27 0.69 0.31
## being_aware_of_things 0.30 0.34 0.33 0.03 0.64 0.36
## getting_hungry -0.06 -0.13 0.90 0.01 0.69 0.31
## feeling_pain 0.00 -0.07 0.90 0.03 0.75 0.25
## feeling_tired 0.00 -0.06 0.88 0.02 0.72 0.28
## feeling_thirsty 0.01 0.02 0.84 -0.04 0.72 0.28
## feeling_too_hot_or_too_cold 0.03 0.09 0.77 0.02 0.72 0.28
## feeling_physically_uncomfortable 0.05 -0.02 0.76 0.18 0.71 0.29
## hearing_sounds -0.04 0.21 0.75 -0.13 0.69 0.31
## being_comforted_by_physical_touch -0.03 0.17 0.72 -0.09 0.63 0.37
## feeling_distressed 0.07 0.00 0.49 0.45 0.66 0.34
## seeing 0.01 0.46 0.47 -0.22 0.57 0.43
## feeling_calm 0.09 0.35 0.36 0.12 0.56 0.44
## feeling_helpless 0.34 0.13 0.12 0.43 0.67 0.33
## feeling_overwhelmed 0.22 0.25 0.12 0.42 0.65 0.35
## feeling_frustrated 0.12 0.37 0.19 0.39 0.73 0.27
## feeling_annoyed 0.24 0.37 0.07 0.38 0.75 0.25
## feeling_neglected 0.16 0.26 0.25 0.36 0.64 0.36
## com
## planning 1.0
## having_self_control 1.0
## thinking_before_they_act 1.0
## having_goals 1.0
## reasoning_about_things 1.0
## controlling_their_emotions 1.0
## telling_right_from_wrong 1.0
## understanding_what_somebody_else_is_thinking 1.0
## focusing_on_a_goal 1.0
## feeling_guilty 1.0
## feeling_embarrassed 1.0
## feeling_pride 1.0
## making_choices 1.4
## calming_themselves_down 1.3
## detecting_danger 1.1
## feeling_hopeless 1.3
## remembering_things 2.1
## imagining_things 1.9
## recognizing_others_emotions 1.9
## feeling_worried 2.6
## getting_hurt_feelings 2.4
## having_wants_and_desires 3.6
## feeling_excited 1.0
## finding_something_funny 1.0
## loving_somebody 1.1
## learning_from_other_people 1.0
## feeling_happy 1.1
## feeling_loved 1.0
## recognizing_somebody_else 1.1
## getting_pleasure_from_music 1.2
## being_afraid_of_somebody 1.4
## listening_to_somebody 1.3
## having_thoughts 1.5
## feeling_sad 1.5
## feeling_safe 1.5
## feeling_textures_(for_example,_smooth,_rough) 1.8
## getting_angry 1.8
## feeling_pleasure 1.9
## being_angry_at_somebody 2.6
## feeling_lonely 2.4
## feeling_bored 2.7
## feeling_confused 2.4
## feeling_scared 2.8
## being_aware_of_things 3.0
## getting_hungry 1.1
## feeling_pain 1.0
## feeling_tired 1.0
## feeling_thirsty 1.0
## feeling_too_hot_or_too_cold 1.0
## feeling_physically_uncomfortable 1.1
## hearing_sounds 1.2
## being_comforted_by_physical_touch 1.2
## feeling_distressed 2.0
## seeing 2.4
## feeling_calm 2.4
## feeling_helpless 2.3
## feeling_overwhelmed 2.4
## feeling_frustrated 2.7
## feeling_annoyed 2.7
## feeling_neglected 3.2
##
## MR1 MR3 MR2 MR4
## SS loadings 16.45 14.29 8.47 4.25
## Proportion Var 0.27 0.24 0.14 0.07
## Cumulative Var 0.27 0.51 0.65 0.72
## Proportion Explained 0.38 0.33 0.19 0.10
## Cumulative Proportion 0.38 0.71 0.90 1.00
##
## With factor correlations of
## MR1 MR3 MR2 MR4
## MR1 1.00 0.65 0.19 0.49
## MR3 0.65 1.00 0.59 0.50
## MR2 0.19 0.59 1.00 0.37
## MR4 0.49 0.50 0.37 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 1770 and the objective function was 73.6 with Chi Square of 64855.87
## The degrees of freedom for the model are 1536 and the objective function was 5.71
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 903 with the empirical chi square 942.37 with prob < 1
## The total number of observations was 903 with Likelihood Chi Square = 5012.16 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.936
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.049 0.052
## BIC = -5441.43
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2 MR4
## Correlation of (regression) scores with factors 0.99 0.99 0.98 0.93
## Multiple R square of scores with factors 0.99 0.97 0.96 0.87
## Minimum correlation of possible factor scores 0.97 0.94 0.92 0.74## Warning in if (is.na(factor_names)) {: the condition has length > 1 and only the
## first element will be used## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
Factor loadings from an exploratory factor analysis of participants’ capacity attributions to newborns, 9-month-old infants, and 5-year-old children in Study 1.
## Saving 4.5 x 5.4 in image## Saving 4.5 x 5.4 in imageMinimizing BIC
## Warning in GPFoblq(L, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.## Factor Analysis using method = minres
## Call: fa(r = d1_all, nfactors = s1_minbic$nfact, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR5 MR4 MR6
## being_afraid_of_somebody 0.02 0.13 0.26 0.24 0.19
## being_angry_at_somebody 0.37 0.05 0.10 0.16 0.10
## being_aware_of_things 0.30 0.24 -0.02 0.23 0.36
## being_comforted_by_physical_touch 0.01 0.66 0.29 0.01 0.03
## calming_themselves_down 0.71 0.00 0.18 -0.02 0.08
## controlling_their_emotions 0.93 0.00 0.06 -0.09 -0.04
## detecting_danger 0.65 0.08 -0.02 0.13 0.04
## feeling_annoyed 0.21 0.06 0.09 0.35 0.08
## feeling_bored 0.31 0.07 0.11 0.20 0.11
## feeling_calm 0.10 0.25 0.33 0.27 0.11
## feeling_confused 0.13 0.02 0.09 0.44 0.19
## feeling_distressed 0.00 0.29 0.01 0.65 0.00
## feeling_embarrassed 0.82 0.05 -0.04 0.00 -0.03
## feeling_excited 0.05 0.01 0.38 0.11 0.35
## feeling_frustrated 0.10 0.11 0.17 0.44 0.09
## feeling_guilty 0.87 0.10 -0.04 -0.01 -0.08
## feeling_happy -0.06 0.13 0.50 0.13 0.30
## feeling_helpless 0.30 0.03 0.20 0.44 -0.11
## feeling_hopeless 0.64 -0.02 0.09 0.22 -0.13
## feeling_lonely 0.15 0.04 0.38 0.35 -0.02
## feeling_loved 0.02 0.06 0.89 -0.03 -0.04
## feeling_neglected 0.14 0.16 0.35 0.38 -0.09
## feeling_overwhelmed 0.17 -0.01 0.14 0.54 0.06
## feeling_pain -0.01 0.86 -0.02 0.05 -0.02
## feeling_physically_uncomfortable 0.01 0.61 -0.08 0.37 0.11
## feeling_pleasure 0.06 0.18 0.28 0.32 0.22
## feeling_pride 0.82 -0.05 0.03 0.07 0.00
## feeling_sad 0.02 0.16 0.39 0.20 0.07
## feeling_safe 0.05 0.17 0.57 0.21 0.06
## feeling_scared -0.07 0.31 0.17 0.33 0.15
## feeling_textures_(for_example,_smooth,_rough) 0.08 0.31 0.15 0.13 0.38
## feeling_thirsty 0.01 0.85 -0.01 -0.03 0.03
## feeling_tired 0.00 0.83 0.05 0.05 -0.05
## feeling_too_hot_or_too_cold 0.03 0.68 0.02 0.18 0.14
## feeling_worried 0.39 0.03 0.14 0.29 -0.03
## finding_something_funny 0.11 0.06 0.29 -0.02 0.37
## focusing_on_a_goal 0.89 -0.05 0.01 0.07 0.07
## getting_angry 0.08 0.08 0.23 0.25 0.09
## getting_hungry -0.05 0.94 0.01 -0.09 -0.16
## getting_hurt_feelings 0.41 0.06 0.21 0.07 0.03
## getting_pleasure_from_music 0.13 0.12 0.33 0.17 0.29
## having_goals 0.94 -0.05 0.00 0.08 -0.02
## having_self_control 0.96 -0.01 0.01 -0.04 0.00
## having_thoughts 0.30 0.10 0.27 0.09 0.27
## having_wants_and_desires 0.27 0.10 0.13 0.33 0.07
## hearing_sounds -0.02 0.72 -0.01 0.00 0.25
## imagining_things 0.53 0.04 0.17 0.00 0.15
## learning_from_other_people 0.17 0.00 0.31 0.09 0.38
## listening_to_somebody 0.21 0.14 0.19 0.07 0.35
## loving_somebody 0.12 -0.06 0.66 0.02 0.11
## making_choices 0.70 -0.04 0.06 0.04 0.22
## planning 1.00 -0.04 -0.05 0.02 0.00
## reasoning_about_things 0.94 -0.01 0.01 -0.03 0.02
## recognizing_others_emotions 0.52 -0.04 0.16 0.08 0.19
## recognizing_somebody_else 0.12 0.22 0.29 -0.05 0.38
## remembering_things 0.56 0.04 0.16 -0.05 0.28
## seeing 0.06 0.49 0.06 -0.10 0.39
## telling_right_from_wrong 0.91 0.07 -0.04 -0.05 -0.02
## thinking_before_they_act 0.96 0.01 0.01 -0.02 0.00
## understanding_what_somebody_else_is_thinking 0.89 -0.04 -0.03 0.02 -0.02
## MR3 h2 u2 com
## being_afraid_of_somebody 0.32 0.76 0.24 3.9
## being_angry_at_somebody 0.40 0.83 0.17 2.6
## being_aware_of_things 0.01 0.68 0.32 3.6
## being_comforted_by_physical_touch -0.20 0.66 0.34 1.6
## calming_themselves_down -0.02 0.71 0.29 1.2
## controlling_their_emotions -0.02 0.79 0.21 1.0
## detecting_danger 0.11 0.68 0.32 1.2
## feeling_annoyed 0.37 0.77 0.23 2.9
## feeling_bored 0.36 0.78 0.22 3.1
## feeling_calm -0.11 0.60 0.40 3.6
## feeling_confused 0.24 0.72 0.28 2.3
## feeling_distressed 0.00 0.70 0.30 1.4
## feeling_embarrassed 0.20 0.85 0.15 1.1
## feeling_excited 0.20 0.76 0.24 2.8
## feeling_frustrated 0.22 0.74 0.26 2.2
## feeling_guilty 0.17 0.86 0.14 1.1
## feeling_happy 0.01 0.71 0.29 2.0
## feeling_helpless 0.11 0.67 0.33 2.5
## feeling_hopeless 0.12 0.72 0.28 1.5
## feeling_lonely 0.14 0.71 0.29 2.6
## feeling_loved 0.01 0.81 0.19 1.0
## feeling_neglected 0.06 0.67 0.33 2.8
## feeling_overwhelmed 0.10 0.67 0.33 1.4
## feeling_pain 0.03 0.76 0.24 1.0
## feeling_physically_uncomfortable -0.06 0.73 0.27 1.8
## feeling_pleasure -0.02 0.67 0.33 3.5
## feeling_pride 0.08 0.83 0.17 1.0
## feeling_sad 0.24 0.70 0.30 2.7
## feeling_safe -0.09 0.69 0.31 1.6
## feeling_scared 0.19 0.69 0.31 3.7
## feeling_textures_(for_example,_smooth,_rough) 0.01 0.68 0.32 2.6
## feeling_thirsty 0.06 0.74 0.26 1.0
## feeling_tired -0.02 0.73 0.27 1.0
## feeling_too_hot_or_too_cold -0.07 0.72 0.28 1.2
## feeling_worried 0.26 0.75 0.25 3.0
## finding_something_funny 0.33 0.78 0.22 3.2
## focusing_on_a_goal -0.11 0.82 0.18 1.1
## getting_angry 0.40 0.75 0.25 2.7
## getting_hungry 0.08 0.76 0.24 1.1
## getting_hurt_feelings 0.32 0.75 0.25 2.6
## getting_pleasure_from_music 0.02 0.68 0.32 3.2
## having_goals -0.10 0.84 0.16 1.0
## having_self_control -0.04 0.86 0.14 1.0
## having_thoughts 0.09 0.72 0.28 3.6
## having_wants_and_desires 0.12 0.60 0.40 2.9
## hearing_sounds 0.00 0.70 0.30 1.2
## imagining_things 0.19 0.75 0.25 1.7
## learning_from_other_people 0.17 0.77 0.23 2.9
## listening_to_somebody 0.12 0.64 0.36 3.1
## loving_somebody 0.16 0.77 0.23 1.2
## making_choices 0.07 0.83 0.17 1.2
## planning -0.11 0.87 0.13 1.0
## reasoning_about_things -0.03 0.84 0.16 1.0
## recognizing_others_emotions 0.09 0.68 0.32 1.6
## recognizing_somebody_else 0.18 0.74 0.26 3.4
## remembering_things 0.10 0.77 0.23 1.7
## seeing 0.04 0.59 0.41 2.1
## telling_right_from_wrong 0.11 0.86 0.14 1.1
## thinking_before_they_act -0.06 0.85 0.15 1.0
## understanding_what_somebody_else_is_thinking 0.02 0.78 0.22 1.0
##
## MR1 MR2 MR5 MR4 MR6 MR3
## SS loadings 16.56 7.47 7.04 5.69 4.08 3.67
## Proportion Var 0.28 0.12 0.12 0.09 0.07 0.06
## Cumulative Var 0.28 0.40 0.52 0.61 0.68 0.74
## Proportion Explained 0.37 0.17 0.16 0.13 0.09 0.08
## Cumulative Proportion 0.37 0.54 0.70 0.83 0.92 1.00
##
## With factor correlations of
## MR1 MR2 MR5 MR4 MR6 MR3
## MR1 1.00 0.14 0.56 0.52 0.41 0.53
## MR2 0.14 1.00 0.51 0.51 0.42 0.26
## MR5 0.56 0.51 1.00 0.55 0.58 0.49
## MR4 0.52 0.51 0.55 1.00 0.36 0.41
## MR6 0.41 0.42 0.58 0.36 1.00 0.32
## MR3 0.53 0.26 0.49 0.41 0.32 1.00
##
## Mean item complexity = 2
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 1770 and the objective function was 73.6 with Chi Square of 64855.87
## The degrees of freedom for the model are 1425 and the objective function was 4.2
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 903 with the empirical chi square 534.58 with prob < 1
## The total number of observations was 903 with Likelihood Chi Square = 3684.27 with prob < 2e-199
##
## Tucker Lewis Index of factoring reliability = 0.955
## RMSEA index = 0.042 and the 90 % confidence intervals are 0.04 0.044
## BIC = -6013.88
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR5 MR4 MR6 MR3
## Correlation of (regression) scores with factors 0.99 0.98 0.96 0.94 0.92 0.92
## Multiple R square of scores with factors 0.99 0.95 0.93 0.89 0.85 0.85
## Minimum correlation of possible factor scores 0.97 0.91 0.85 0.78 0.71 0.70## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 5.4 in image## Saving 4.5 x 5.4 in imageWeisman et al.’s (2017) factor retention criteria
## [1] 2## Factor Analysis using method = minres
## Call: fa(r = d1_all, nfactors = s1_weismanetal, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## being_afraid_of_somebody 0.38 0.60 0.71 0.29 1.7
## being_angry_at_somebody 0.69 0.32 0.77 0.23 1.4
## being_aware_of_things 0.40 0.54 0.63 0.37 1.8
## being_comforted_by_physical_touch -0.16 0.81 0.58 0.42 1.1
## calming_themselves_down 0.80 0.07 0.69 0.31 1.0
## controlling_their_emotions 0.92 -0.13 0.76 0.24 1.0
## detecting_danger 0.75 0.13 0.66 0.34 1.1
## feeling_annoyed 0.56 0.43 0.70 0.30 1.9
## feeling_bored 0.62 0.37 0.73 0.27 1.6
## feeling_calm 0.22 0.63 0.57 0.43 1.2
## feeling_confused 0.48 0.48 0.67 0.33 2.0
## feeling_distressed 0.14 0.66 0.54 0.46 1.1
## feeling_embarrassed 0.91 -0.01 0.82 0.18 1.0
## feeling_excited 0.42 0.54 0.67 0.33 1.9
## feeling_frustrated 0.41 0.56 0.68 0.32 1.8
## feeling_guilty 0.91 -0.01 0.81 0.19 1.0
## feeling_happy 0.22 0.68 0.63 0.37 1.2
## feeling_helpless 0.56 0.34 0.59 0.41 1.6
## feeling_hopeless 0.80 0.05 0.68 0.32 1.0
## feeling_lonely 0.48 0.48 0.65 0.35 2.0
## feeling_loved 0.33 0.56 0.58 0.42 1.6
## feeling_neglected 0.37 0.54 0.60 0.40 1.8
## feeling_overwhelmed 0.48 0.42 0.58 0.42 2.0
## feeling_pain -0.24 0.88 0.65 0.35 1.1
## feeling_physically_uncomfortable -0.07 0.82 0.63 0.37 1.0
## feeling_pleasure 0.28 0.64 0.64 0.36 1.4
## feeling_pride 0.93 -0.05 0.82 0.18 1.0
## feeling_sad 0.34 0.61 0.67 0.33 1.6
## feeling_safe 0.26 0.63 0.62 0.38 1.3
## feeling_scared 0.15 0.74 0.68 0.32 1.1
## feeling_textures_(for_example,_smooth,_rough) 0.20 0.69 0.63 0.37 1.2
## feeling_thirsty -0.20 0.86 0.63 0.37 1.1
## feeling_tired -0.22 0.87 0.63 0.37 1.1
## feeling_too_hot_or_too_cold -0.10 0.85 0.66 0.34 1.0
## feeling_worried 0.67 0.29 0.71 0.29 1.4
## finding_something_funny 0.46 0.50 0.66 0.34 2.0
## focusing_on_a_goal 0.92 -0.08 0.78 0.22 1.0
## getting_angry 0.47 0.50 0.68 0.32 2.0
## getting_hungry -0.33 0.84 0.57 0.43 1.3
## getting_hurt_feelings 0.68 0.29 0.72 0.28 1.3
## getting_pleasure_from_music 0.37 0.57 0.64 0.36 1.7
## having_goals 0.94 -0.12 0.80 0.20 1.0
## having_self_control 0.96 -0.13 0.82 0.18 1.0
## having_thoughts 0.52 0.45 0.69 0.31 2.0
## having_wants_and_desires 0.48 0.41 0.58 0.42 1.9
## hearing_sounds -0.17 0.85 0.63 0.37 1.1
## imagining_things 0.74 0.22 0.73 0.27 1.2
## learning_from_other_people 0.51 0.47 0.69 0.31 2.0
## listening_to_somebody 0.42 0.50 0.60 0.40 1.9
## loving_somebody 0.50 0.44 0.64 0.36 2.0
## making_choices 0.86 0.09 0.81 0.19 1.0
## planning 0.97 -0.18 0.82 0.18 1.1
## reasoning_about_things 0.94 -0.11 0.81 0.19 1.0
## recognizing_others_emotions 0.72 0.18 0.67 0.33 1.1
## recognizing_somebody_else 0.33 0.60 0.66 0.34 1.6
## remembering_things 0.72 0.23 0.73 0.27 1.2
## seeing 0.03 0.69 0.49 0.51 1.0
## telling_right_from_wrong 0.93 -0.04 0.83 0.17 1.0
## thinking_before_they_act 0.94 -0.10 0.81 0.19 1.0
## understanding_what_somebody_else_is_thinking 0.93 -0.14 0.76 0.24 1.0
##
## MR1 MR2
## SS loadings 23.33 17.58
## Proportion Var 0.39 0.29
## Cumulative Var 0.39 0.68
## Proportion Explained 0.57 0.43
## Cumulative Proportion 0.57 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 0.44
## MR2 0.44 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 1770 and the objective function was 73.6 with Chi Square of 64855.87
## The degrees of freedom for the model are 1651 and the objective function was 10.52
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 903 with the empirical chi square 3597.69 with prob < 4e-146
## The total number of observations was 903 with Likelihood Chi Square = 9258.55 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.871
## RMSEA index = 0.071 and the 90 % confidence intervals are 0.07 0.073
## BIC = -1977.7
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.99 0.99
## Multiple R square of scores with factors 0.99 0.98
## Minimum correlation of possible factor scores 0.98 0.95## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 5.4 in image## Saving 4.5 x 5.4 in imageStudy 2
Exploratory factor analyses (EFAs)
Parallel analysis
## Parallel analysis suggests that the number of factors = 4 and the number of components = 4## Factor Analysis using method = minres
## Call: fa(r = d2_all, nfactors = s2_parallel$nfact, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 h2 u2 com
## having_self_control 0.96 -0.02 -0.01 0.01 0.89 0.11 1.0
## controlling_their_emotions 0.94 -0.04 0.01 -0.01 0.85 0.15 1.0
## telling_right_from_wrong 0.93 -0.03 0.04 0.00 0.88 0.12 1.0
## planning 0.90 0.07 -0.05 0.00 0.82 0.18 1.0
## reasoning_about_things 0.89 0.05 0.02 0.00 0.86 0.14 1.0
## feeling_overwhelmed 0.02 0.84 0.10 -0.06 0.77 0.23 1.0
## feeling_distressed -0.01 0.81 -0.09 0.19 0.75 0.25 1.1
## feeling_frustrated 0.03 0.80 0.08 0.03 0.78 0.22 1.0
## feeling_helpless 0.08 0.77 0.09 -0.05 0.70 0.30 1.1
## feeling_lonely 0.05 0.60 0.27 0.03 0.70 0.30 1.4
## feeling_happy -0.07 0.05 0.85 0.09 0.78 0.22 1.0
## finding_something_funny 0.09 -0.01 0.83 0.02 0.78 0.22 1.0
## feeling_excited -0.01 0.16 0.79 -0.01 0.78 0.22 1.1
## loving_somebody 0.15 0.07 0.66 0.01 0.63 0.37 1.1
## learning_from_other_people 0.31 0.03 0.48 0.05 0.55 0.45 1.7
## getting_hungry -0.01 -0.06 0.02 0.88 0.73 0.27 1.0
## feeling_pain 0.03 0.04 -0.01 0.86 0.78 0.22 1.0
## feeling_tired -0.01 0.16 0.02 0.72 0.69 0.31 1.1
## hearing_sounds 0.01 -0.12 0.29 0.67 0.57 0.43 1.4
## feeling_physically_uncomfortable 0.02 0.41 -0.12 0.56 0.64 0.36 2.0
##
## MR2 MR1 MR4 MR3
## SS loadings 4.64 3.74 3.42 3.15
## Proportion Var 0.23 0.19 0.17 0.16
## Cumulative Var 0.23 0.42 0.59 0.75
## Proportion Explained 0.31 0.25 0.23 0.21
## Cumulative Proportion 0.31 0.56 0.79 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3
## MR2 1.00 0.43 0.54 0.09
## MR1 0.43 1.00 0.60 0.54
## MR4 0.54 0.60 1.00 0.40
## MR3 0.09 0.54 0.40 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 19.29 with Chi Square of 76082.56
## The degrees of freedom for the model are 116 and the objective function was 0.34
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 3952 with the empirical chi square 187.9 with prob < 2.7e-05
## The total number of observations was 3952 with Likelihood Chi Square = 1332.54 with prob < 1e-205
##
## Tucker Lewis Index of factoring reliability = 0.974
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.049 0.054
## BIC = 371.83
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1 MR4 MR3
## Correlation of (regression) scores with factors 0.98 0.97 0.96 0.96
## Multiple R square of scores with factors 0.97 0.93 0.92 0.91
## Minimum correlation of possible factor scores 0.94 0.86 0.85 0.83## Warning in if (is.na(factor_names)) {: the condition has length > 1 and only the
## first element will be used## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 2.25 in image## Saving 4.5 x 2.25 in imageMinimizing BIC
## Warning in GPFoblq(L, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.## Factor Analysis using method = minres
## Call: fa(r = d2_all, nfactors = s2_minbic$nfact, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR3 MR4 MR1 MR5 MR6 MR7
## controlling_their_emotions 0.96 0.01 0.03 -0.03 0.00 -0.06 -0.04
## feeling_distressed 0.02 0.05 -0.01 0.84 0.02 0.02 0.04
## feeling_excited 0.04 0.00 0.90 0.04 0.02 -0.05 0.02
## feeling_frustrated 0.06 0.05 0.17 0.66 0.07 -0.01 0.01
## feeling_happy -0.03 0.05 0.76 0.06 0.06 0.07 -0.05
## feeling_helpless 0.06 0.04 0.01 0.17 0.63 -0.01 0.04
## feeling_lonely 0.01 0.02 0.04 -0.02 0.88 0.02 0.00
## feeling_overwhelmed 0.02 0.00 0.09 0.57 0.21 0.08 0.01
## feeling_pain 0.02 0.76 0.02 -0.02 0.10 -0.04 0.12
## feeling_physically_uncomfortable -0.01 0.38 0.01 0.30 0.08 0.00 0.30
## feeling_tired 0.01 0.71 0.02 0.19 0.02 -0.02 -0.03
## finding_something_funny 0.08 0.03 0.63 -0.05 0.06 0.23 0.03
## getting_hungry -0.01 0.94 -0.05 -0.02 -0.01 0.02 -0.05
## having_self_control 0.98 0.02 0.01 0.00 -0.01 -0.04 -0.04
## hearing_sounds 0.00 0.63 0.24 -0.06 -0.04 0.08 0.06
## learning_from_other_people 0.19 0.04 0.11 0.08 0.03 0.52 0.06
## loving_somebody 0.10 0.05 0.21 0.07 0.22 0.40 -0.18
## planning 0.88 -0.03 -0.04 0.04 0.03 0.03 0.05
## reasoning_about_things 0.86 -0.02 -0.01 0.00 0.04 0.08 0.08
## telling_right_from_wrong 0.92 0.00 0.01 0.00 -0.02 0.04 -0.01
## MR8 h2 u2 com
## controlling_their_emotions -0.01 0.86 0.14 1.0
## feeling_distressed -0.11 0.82 0.18 1.0
## feeling_excited 0.06 0.86 0.14 1.0
## feeling_frustrated 0.12 0.80 0.20 1.3
## feeling_happy -0.12 0.80 0.20 1.1
## feeling_helpless 0.18 0.75 0.25 1.4
## feeling_lonely -0.07 0.83 0.17 1.0
## feeling_overwhelmed 0.19 0.78 0.22 1.6
## feeling_pain -0.08 0.78 0.22 1.1
## feeling_physically_uncomfortable -0.08 0.70 0.30 3.1
## feeling_tired -0.02 0.70 0.30 1.2
## finding_something_funny 0.02 0.77 0.23 1.4
## getting_hungry 0.04 0.77 0.23 1.0
## having_self_control -0.01 0.90 0.10 1.0
## hearing_sounds -0.02 0.57 0.43 1.4
## learning_from_other_people 0.03 0.64 0.36 1.5
## loving_somebody -0.08 0.70 0.30 3.1
## planning 0.00 0.82 0.18 1.0
## reasoning_about_things 0.02 0.86 0.14 1.0
## telling_right_from_wrong 0.00 0.88 0.12 1.0
##
## MR2 MR3 MR4 MR1 MR5 MR6 MR7 MR8
## SS loadings 4.55 2.91 2.55 2.29 1.90 0.94 0.28 0.18
## Proportion Var 0.23 0.15 0.13 0.11 0.09 0.05 0.01 0.01
## Cumulative Var 0.23 0.37 0.50 0.61 0.71 0.76 0.77 0.78
## Proportion Explained 0.29 0.19 0.16 0.15 0.12 0.06 0.02 0.01
## Cumulative Proportion 0.29 0.48 0.64 0.79 0.91 0.97 0.99 1.00
##
## With factor correlations of
## MR2 MR3 MR4 MR1 MR5 MR6 MR7 MR8
## MR2 1.00 0.09 0.49 0.34 0.49 0.54 0.08 0.15
## MR3 0.09 1.00 0.42 0.54 0.47 0.21 0.37 -0.25
## MR4 0.49 0.42 1.00 0.51 0.68 0.66 0.01 0.04
## MR1 0.34 0.54 0.51 1.00 0.80 0.23 0.36 0.11
## MR5 0.49 0.47 0.68 0.80 1.00 0.46 0.22 0.08
## MR6 0.54 0.21 0.66 0.23 0.46 1.00 0.00 -0.05
## MR7 0.08 0.37 0.01 0.36 0.22 0.00 1.00 -0.04
## MR8 0.15 -0.25 0.04 0.11 0.08 -0.05 -0.04 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 8 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 19.29 with Chi Square of 76082.56
## The degrees of freedom for the model are 58 and the objective function was 0.08
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 3952 with the empirical chi square 25.8 with prob < 1
## The total number of observations was 3952 with Likelihood Chi Square = 326.4 with prob < 4.7e-39
##
## Tucker Lewis Index of factoring reliability = 0.988
## RMSEA index = 0.034 and the 90 % confidence intervals are 0.031 0.038
## BIC = -153.95
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR3 MR4 MR1 MR5 MR6
## Correlation of (regression) scores with factors 0.99 0.96 0.96 0.96 0.95 0.86
## Multiple R square of scores with factors 0.97 0.91 0.93 0.91 0.91 0.73
## Minimum correlation of possible factor scores 0.94 0.82 0.85 0.82 0.81 0.47
## MR7 MR8
## Correlation of (regression) scores with factors 0.69 0.67
## Multiple R square of scores with factors 0.48 0.46
## Minimum correlation of possible factor scores -0.04 -0.09## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 5.5 x 1.92 in image## Saving 5.5 x 1.92 in imageWeisman et al.’s (2017) factor retention criteria
## [1] 4## Factor Analysis using method = minres
## Call: fa(r = d2_all, nfactors = s2_weismanetal, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 h2 u2 com
## controlling_their_emotions 0.94 -0.04 0.01 -0.01 0.85 0.15 1.0
## feeling_distressed -0.01 0.81 -0.09 0.19 0.75 0.25 1.1
## feeling_excited -0.01 0.16 0.79 -0.01 0.78 0.22 1.1
## feeling_frustrated 0.03 0.80 0.08 0.03 0.78 0.22 1.0
## feeling_happy -0.07 0.05 0.85 0.09 0.78 0.22 1.0
## feeling_helpless 0.08 0.77 0.09 -0.05 0.70 0.30 1.1
## feeling_lonely 0.05 0.60 0.27 0.03 0.70 0.30 1.4
## feeling_overwhelmed 0.02 0.84 0.10 -0.06 0.77 0.23 1.0
## feeling_pain 0.03 0.04 -0.01 0.86 0.78 0.22 1.0
## feeling_physically_uncomfortable 0.02 0.41 -0.12 0.56 0.64 0.36 2.0
## feeling_tired -0.01 0.16 0.02 0.72 0.69 0.31 1.1
## finding_something_funny 0.09 -0.01 0.83 0.02 0.78 0.22 1.0
## getting_hungry -0.01 -0.06 0.02 0.88 0.73 0.27 1.0
## having_self_control 0.96 -0.02 -0.01 0.01 0.89 0.11 1.0
## hearing_sounds 0.01 -0.12 0.29 0.67 0.57 0.43 1.4
## learning_from_other_people 0.31 0.03 0.48 0.05 0.55 0.45 1.7
## loving_somebody 0.15 0.07 0.66 0.01 0.63 0.37 1.1
## planning 0.90 0.07 -0.05 0.00 0.82 0.18 1.0
## reasoning_about_things 0.89 0.05 0.02 0.00 0.86 0.14 1.0
## telling_right_from_wrong 0.93 -0.03 0.04 0.00 0.88 0.12 1.0
##
## MR2 MR1 MR4 MR3
## SS loadings 4.64 3.74 3.42 3.15
## Proportion Var 0.23 0.19 0.17 0.16
## Cumulative Var 0.23 0.42 0.59 0.75
## Proportion Explained 0.31 0.25 0.23 0.21
## Cumulative Proportion 0.31 0.56 0.79 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3
## MR2 1.00 0.43 0.54 0.09
## MR1 0.43 1.00 0.60 0.54
## MR4 0.54 0.60 1.00 0.40
## MR3 0.09 0.54 0.40 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 19.29 with Chi Square of 76082.56
## The degrees of freedom for the model are 116 and the objective function was 0.34
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 3952 with the empirical chi square 187.9 with prob < 2.7e-05
## The total number of observations was 3952 with Likelihood Chi Square = 1332.54 with prob < 1e-205
##
## Tucker Lewis Index of factoring reliability = 0.974
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.049 0.054
## BIC = 371.83
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1 MR4 MR3
## Correlation of (regression) scores with factors 0.98 0.97 0.96 0.96
## Multiple R square of scores with factors 0.97 0.93 0.92 0.91
## Minimum correlation of possible factor scores 0.94 0.86 0.85 0.83## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 2.25 in image## Saving 4.5 x 2.25 in imageDevelopmental trajectories
## Joining, by = "capacity"## `summarise()` has grouped output by 'factor'. You can override using the `.groups` argument.##
## Family: Beta regression
## Link function: logit
##
## Formula:
## response ~ s(target_year, by = factor) + factor
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.26325 0.07696 -16.41 <2e-16 ***
## factorbod_cog 4.82455 0.05306 90.92 <2e-16 ***
## factorneg_cog 3.05376 0.05003 61.04 <2e-16 ***
## factorsoc_cog 3.13147 0.04991 62.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(target_year):factorBodily sensations 6.072 6.072 364.8 <2e-16 ***
## s(target_year):factorNegative affect 7.700 7.700 1535.9 <2e-16 ***
## s(target_year):factorSocial connection 8.626 8.626 2351.3 <2e-16 ***
## s(target_year):factorCognition and control 8.134 8.134 3396.9 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.504
## Scale est. = 0.30004 n = 79040## `summarise()` has grouped output by 'ResponseId', 'factor'. You can override using the `.groups` argument.Study 3
Developmental trajectories
## `summarise()` has grouped output by 'factor'. You can override using the `.groups` argument.##
## Family: Beta regression
## Link function: logit
##
## Formula:
## response ~ s(target_year, by = factor) + factor
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.45908 0.09301 -15.69 <2e-16 ***
## factorbod_cog 6.00076 0.11381 52.73 <2e-16 ***
## factorneg_cog 3.48665 0.10836 32.18 <2e-16 ***
## factorsoc_cog 3.14862 0.10752 29.28 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(target_year):factorBodily sensations 8.127 8.127 176.0 <2e-16 ***
## s(target_year):factorNegative affect 8.345 8.345 748.5 <2e-16 ***
## s(target_year):factorSocial connection 8.853 8.853 1744.7 <2e-16 ***
## s(target_year):factorCognition and control 8.445 8.445 1782.2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.52
## Scale est. = 0.19709 n = 31304## `summarise()` has grouped output by 'ResponseId', 'factor'. You can override using the `.groups` argument.## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Perceived developmental trajectories for four domains of mental life (Studies 2-3). Lighter lines represent individual participants’ responses, black points correspond to mean responses across the sample, error bars are bootstrapped 95% confidence intervals, and thick red lines are predictions from our generalized additive models (beta regressions). In Study 2 (Panel A), participants assessed 5 capacities within each domain, and assessed all capacities for a given target age before moving on to the next target age. In Study 3 (Panel B), participants assessed 2 capacities within each domain, and assessed a single capacity for all target ages before moving on to the next capacity.
## Saving 4 x 4.8 in image## Saving 4 x 4.8 in imageComparing variability in ratings across domains
## `summarise()` has grouped output by 'factor', 'capacity'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'factor', 'capacity'. You can override using the `.groups` argument.## Warning: Removed 4 row(s) containing missing values (geom_path).## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_segment).
Variance of capacity attributions in four domains of mental life (Studies 2-3). Lighter dots and lines represent variance across participnts for a particular capacity at a particular target age, red points correpond to mean variance across capacities at birth, black points correspond to mean variance across capacities and across all target ages, and error bars are bootstrapped 95% confidence intervals. In Study 2 (Panel A), participants assessed 5 capacities within each domain, and assessed all capacities for a given target age before moving on to the next target age. In Study 3 (Panel B), participants assessed 2 capacities within each domain, and assessed a single capacity for all target ages before moving on to the next capacity.
## Saving 4 x 4.8 in image
## Saving 4 x 4.8 in imageDevelopmental mechanisms: Ratings
Dimensionality reduction
Parallel analysis
## Parallel analysis suggests that the number of factors = 4 and the number of components = 2## Factor Analysis using method = minres
## Call: fa(r = d3_mech_ratings_wide, nfactors = s3_parallel$nfact, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR4 MR2 h2 u2 com
## observes_people 0.87 0.33 0.25 -0.06 0.93 0.069 1.5
## interacts_people 0.82 0.37 0.24 -0.09 0.87 0.126 1.6
## observes_objects 0.66 0.26 0.46 0.05 0.72 0.277 2.2
## people_teach 0.35 0.82 0.20 -0.02 0.85 0.154 1.5
## experiments 0.44 0.64 0.33 0.02 0.71 0.287 2.3
## senses_improve 0.32 0.15 0.69 0.22 0.65 0.350 1.8
## body_grows 0.11 0.14 0.55 0.44 0.53 0.474 2.2
## brain_changes 0.42 0.26 0.49 -0.05 0.48 0.518 2.5
## womb_experiences 0.03 0.05 0.14 0.81 0.67 0.328 1.1
## preprogrammed -0.26 -0.29 0.04 0.43 0.33 0.666 2.5
##
## MR1 MR3 MR4 MR2
## SS loadings 2.54 1.60 1.52 1.09
## Proportion Var 0.25 0.16 0.15 0.11
## Cumulative Var 0.25 0.41 0.57 0.68
## Proportion Explained 0.38 0.24 0.22 0.16
## Cumulative Proportion 0.38 0.61 0.84 1.00
##
## Mean item complexity = 1.9
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 6.2 with Chi Square of 14888.56
## The degrees of freedom for the model are 11 and the objective function was 0.04
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 2408 with the empirical chi square 21.3 with prob < 0.03
## The total number of observations was 2408 with Likelihood Chi Square = 103.94 with prob < 3e-17
##
## Tucker Lewis Index of factoring reliability = 0.974
## RMSEA index = 0.059 and the 90 % confidence intervals are 0.049 0.07
## BIC = 18.28
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR4 MR2
## Correlation of (regression) scores with factors 0.93 0.89 0.79 0.83
## Multiple R square of scores with factors 0.87 0.78 0.62 0.70
## Minimum correlation of possible factor scores 0.73 0.57 0.25 0.39## Warning in if (is.na(factor_names)) {: the condition has length > 1 and only the
## first element will be used## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 1.8 in image## Saving 4.5 x 1.8 in imageMinimizing BIC
## Factor Analysis using method = minres
## Call: fa(r = d3_mech_ratings_wide, nfactors = s3_minbic$nfact, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 MR5 MR4 h2 u2 com
## preprogrammed -0.25 -0.28 0.43 0.05 -0.10 0.34 0.664 2.6
## womb_experiences 0.03 0.04 0.80 0.07 0.00 0.65 0.353 1.0
## body_grows 0.12 0.15 0.51 0.40 0.24 0.51 0.488 2.7
## brain_changes 0.36 0.23 0.01 0.27 0.69 0.73 0.266 2.1
## senses_improve 0.33 0.17 0.28 0.63 0.24 0.67 0.330 2.5
## observes_objects 0.68 0.27 0.08 0.45 0.15 0.77 0.235 2.2
## experiments 0.44 0.64 0.05 0.25 0.19 0.71 0.293 2.3
## observes_people 0.86 0.34 -0.03 0.17 0.22 0.93 0.075 1.5
## interacts_people 0.81 0.37 -0.07 0.15 0.23 0.87 0.128 1.7
## people_teach 0.35 0.84 0.00 0.13 0.14 0.86 0.137 1.5
##
## MR1 MR3 MR2 MR5 MR4
## SS loadings 2.48 1.63 1.18 0.96 0.79
## Proportion Var 0.25 0.16 0.12 0.10 0.08
## Cumulative Var 0.25 0.41 0.53 0.62 0.70
## Proportion Explained 0.35 0.23 0.17 0.14 0.11
## Cumulative Proportion 0.35 0.58 0.75 0.89 1.00
##
## Mean item complexity = 2
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 6.2 with Chi Square of 14888.56
## The degrees of freedom for the model are 5 and the objective function was 0.01
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 2408 with the empirical chi square 2.21 with prob < 0.82
## The total number of observations was 2408 with Likelihood Chi Square = 30.85 with prob < 1e-05
##
## Tucker Lewis Index of factoring reliability = 0.984
## RMSEA index = 0.046 and the 90 % confidence intervals are 0.031 0.063
## BIC = -8.08
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2 MR5 MR4
## Correlation of (regression) scores with factors 0.93 0.90 0.84 0.74 0.75
## Multiple R square of scores with factors 0.86 0.80 0.71 0.54 0.56
## Minimum correlation of possible factor scores 0.72 0.61 0.41 0.09 0.12## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 1.8 in image## Saving 4.5 x 1.8 in imageWeisman et al.’s (2017) factor retention criteria
## [1] 2## Factor Analysis using method = minres
## Call: fa(r = d3_mech_ratings_wide, nfactors = s3_weismanetal, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## preprogrammed -0.40 0.44 0.36 0.64 2.0
## womb_experiences 0.00 0.64 0.40 0.60 1.0
## body_grows 0.30 0.68 0.55 0.45 1.4
## brain_changes 0.64 0.18 0.44 0.56 1.2
## senses_improve 0.55 0.51 0.55 0.45 2.0
## observes_objects 0.80 0.23 0.69 0.31 1.2
## experiments 0.79 0.11 0.63 0.37 1.0
## observes_people 0.91 0.01 0.83 0.17 1.0
## interacts_people 0.91 -0.03 0.82 0.18 1.0
## people_teach 0.74 0.02 0.55 0.45 1.0
##
## MR1 MR2
## SS loadings 4.43 1.41
## Proportion Var 0.44 0.14
## Cumulative Var 0.44 0.58
## Proportion Explained 0.76 0.24
## Cumulative Proportion 0.76 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 6.2 with Chi Square of 14888.56
## The degrees of freedom for the model are 26 and the objective function was 0.58
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 2408 with the empirical chi square 374.7 with prob < 1.8e-63
## The total number of observations was 2408 with Likelihood Chi Square = 1392.85 with prob < 9.7e-278
##
## Tucker Lewis Index of factoring reliability = 0.841
## RMSEA index = 0.148 and the 90 % confidence intervals are 0.141 0.154
## BIC = 1190.4
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.97 0.85
## Multiple R square of scores with factors 0.94 0.73
## Minimum correlation of possible factor scores 0.87 0.45## Warning in if (is.na(factor_names)) {: the condition has length > 1 and only the
## first element will be used## Joining, by = "capacity"## Joining, by = "factor"
## Joining, by = "factor"
## Saving 4.5 x 1.8 in image## Saving 4.5 x 1.8 in imageHierarchical clustering
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
## Saving 4.5 x 1.8 in image## Saving 4.5 x 1.8 in imageModel
## `summarise()` has grouped output by 'ResponseId'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ResponseId', 'dev_factor_cluster', 'dev_factor'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ResponseId', 'dev_factor_cluster'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'dev_factor_cluster'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ResponseId', 'dev_factor_cluster', 'factor'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ResponseId'. You can override using the `.groups` argument.## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ dev_factor_cluster * factor + (1 + dev_factor_cluster +
## factor | ResponseId) + (1 | dev_factor) + (1 | capacity)
## Data: d3_mech_ratings
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 84145.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3297 -0.6590 -0.0029 0.6601 3.3378
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ResponseId (Intercept) 0.80191 0.8955
## dev_factor_clusterext_gm 0.09652 0.3107 -0.05
## factorbod_gm 0.39914 0.6318 0.44 -0.14
## factorsoc_gm 0.16402 0.4050 -0.26 0.11 -0.73
## factorcog_gm 0.23591 0.4857 -0.61 -0.03 -0.77 0.49
## dev_factor (Intercept) 0.46449 0.6815
## capacity (Intercept) 0.09151 0.3025
## Residual 2.56034 1.6001
## Number of obs: 21672, groups: ResponseId, 301; dev_factor, 9; capacity, 8
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.09644 0.25783 12.009
## dev_factor_clusterext_gm 0.28487 0.22955 1.241
## factorbod_gm -0.58979 0.18974 -3.108
## factorsoc_gm 0.58616 0.18767 3.123
## factorcog_gm 0.24189 0.18831 1.285
## dev_factor_clusterext_gm:factorbod_gm -1.11876 0.01894 -59.058
## dev_factor_clusterext_gm:factorsoc_gm 0.36077 0.01894 19.044
## dev_factor_clusterext_gm:factorcog_gm 0.88278 0.01894 46.601
##
## Correlation of Fixed Effects:
## (Intr) dv_f__ fctrb_ fctrs_ fctrc_
## dv_fctr_cl_ -0.099
## factorbd_gm 0.017 -0.002
## factorsc_gm -0.006 0.001 -0.342
## factorcg_gm -0.018 0.000 -0.346 -0.318
## dv_fctr_clstrxt_gm:fctrb_ 0.000 0.000 -0.011 0.004 0.004
## dv_fctr_clstrxt_gm:fctrs_ 0.000 0.000 0.004 -0.011 0.004
## dv_fctr_clstrxt_gm:fctrc_ 0.000 0.000 0.004 0.004 -0.011
## dv_fctr_clstrxt_gm:fctrb_ dv_fctr_clstrxt_gm:fctrs_
## dv_fctr_cl_
## factorbd_gm
## factorsc_gm
## factorcg_gm
## dv_fctr_clstrxt_gm:fctrb_
## dv_fctr_clstrxt_gm:fctrs_ -0.333
## dv_fctr_clstrxt_gm:fctrc_ -0.333 -0.333## Linear mixed model fit by REML ['lmerMod']
## Formula:
## response ~ dev_factor_cluster + (1 + dev_factor_cluster | ResponseId) +
## (1 | dev_factor) + (1 | capacity)
## Data: d3_mech_ratings %>% filter(factor == "Bodily sensations")
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 20410.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2099 -0.6107 -0.0514 0.5765 3.7935
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ResponseId (Intercept) 1.638945 1.28021
## dev_factor_clusterext_gm 0.233138 0.48284 0.60
## dev_factor (Intercept) 0.928326 0.96350
## capacity (Intercept) 0.001675 0.04093
## Residual 2.055307 1.43363
## Number of obs: 5418, groups: ResponseId, 301; dev_factor, 9; capacity, 2
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.5066 0.3333 7.520
## dev_factor_clusterext_gm -0.8339 0.3250 -2.566
##
## Correlation of Fixed Effects:
## (Intr)
## dv_fctr_cl_ -0.096## Linear mixed model fit by REML ['lmerMod']
## Formula:
## response ~ dev_factor_cluster + (1 + dev_factor_cluster | ResponseId) +
## (1 | dev_factor) + (1 | capacity)
## Data: d3_mech_ratings %>% filter(factor == "Negative affect")
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 21649.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.04361 -0.68969 -0.03827 0.70000 2.80155
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ResponseId (Intercept) 1.1151187 1.05599
## dev_factor_clusterext_gm 0.2255416 0.47491 0.36
## dev_factor (Intercept) 0.7504263 0.86627
## capacity (Intercept) 0.0007574 0.02752
## Residual 2.6733052 1.63502
## Number of obs: 5418, groups: ResponseId, 301; dev_factor, 9; capacity, 2
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.8582 0.2983 9.580
## dev_factor_clusterext_gm 0.1601 0.2927 0.547
##
## Correlation of Fixed Effects:
## (Intr)
## dv_fctr_cl_ -0.101## Linear mixed model fit by REML ['lmerMod']
## Formula:
## response ~ dev_factor_cluster + (1 + dev_factor_cluster | ResponseId) +
## (1 | dev_factor) + (1 | capacity)
## Data: d3_mech_ratings %>% filter(factor == "Social connection")
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 20877.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6315 -0.5963 0.0956 0.6517 3.1847
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ResponseId (Intercept) 0.8011 0.8950
## dev_factor_clusterext_gm 0.1719 0.4146 -0.16
## dev_factor (Intercept) 0.6350 0.7969
## capacity (Intercept) 0.3074 0.5544
## Residual 2.3440 1.5310
## Number of obs: 5418, groups: ResponseId, 301; dev_factor, 9; capacity, 2
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.6826 0.4777 7.709
## dev_factor_clusterext_gm 0.6456 0.2692 2.399
##
## Correlation of Fixed Effects:
## (Intr)
## dv_fctr_cl_ -0.064## Linear mixed model fit by REML ['lmerMod']
## Formula:
## response ~ dev_factor_cluster + (1 + dev_factor_cluster | ResponseId) +
## (1 | dev_factor) + (1 | capacity)
## Data: d3_mech_ratings %>% filter(factor == "Cognition and control")
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 19561.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8585 -0.5546 0.0667 0.5785 3.6821
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ResponseId (Intercept) 0.57475 0.7581
## dev_factor_clusterext_gm 0.40527 0.6366 -0.26
## dev_factor (Intercept) 0.42184 0.6495
## capacity (Intercept) 0.05676 0.2383
## Residual 1.76819 1.3297
## Number of obs: 5418, groups: ResponseId, 301; dev_factor, 9; capacity, 2
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.3383 0.2794 11.947
## dev_factor_clusterext_gm 1.1676 0.2217 5.268
##
## Correlation of Fixed Effects:
## (Intr)
## dv_fctr_cl_ -0.093## `summarise()` has grouped output by 'ResponseId', 'dev_factor_cluster', 'factor'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ResponseId', 'dev_factor_cluster'. You can override using the `.groups` argument.“Other” responses
Developmental mechanisms: Selection of “most important”
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: extrinsic ~ factor + (1 | ResponseId) + (1 | capacity)
## Data: d3_mech_choice %>% mutate(extrinsic = case_when(dev_factor_cluster ==
## "extrinsic" ~ 1, dev_factor_cluster == "intrinsic" ~ 0, is.na(dev_factor_cluster) ~
## NA_real_, TRUE ~ NA_real_))
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 1578.9 1612.3 -783.5 1566.9 1916
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0902 -0.3463 -0.1044 0.3877 13.5689
##
## Random effects:
## Groups Name Variance Std.Dev.
## ResponseId (Intercept) 2.2531 1.5010
## capacity (Intercept) 0.4484 0.6697
## Number of obs: 1922, groups: ResponseId, 301; capacity, 8
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6927 0.2677 -2.588 0.00966 **
## factorbod_gm -3.7301 0.4711 -7.917 2.43e-15 ***
## factorsoc_gm 1.2286 0.4298 2.858 0.00426 **
## factorcog_gm 2.6981 0.4426 6.096 1.09e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fctrb_ fctrs_
## factorbd_gm 0.088
## factorsc_gm -0.040 -0.362
## factorcg_gm -0.025 -0.389 -0.290## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: extrinsic ~ 1 + (1 | ResponseId) + (1 | capacity)
## Data: d3_mech_choice %>% filter(factor == "Bodily sensations") %>%
## mutate(extrinsic = case_when(dev_factor_cluster == "extrinsic" ~
## 1, dev_factor_cluster == "intrinsic" ~ 0, is.na(dev_factor_cluster) ~
## NA_real_, TRUE ~ NA_real_))
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 139.1 152.2 -66.5 133.1 583
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.93243 -0.01169 -0.01051 -0.01046 1.19891
##
## Random effects:
## Groups Name Variance Std.Dev.
## ResponseId (Intercept) 70.12434 8.3740
## capacity (Intercept) 0.06855 0.2618
## Number of obs: 586, groups: ResponseId, 300; capacity, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.0691 0.7369 -12.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: extrinsic ~ 1 + (1 | ResponseId) + (1 | capacity)
## Data:
## d3_mech_choice %>% filter(factor == "Negative affect") %>% mutate(extrinsic = case_when(dev_factor_cluster ==
## "extrinsic" ~ 1, dev_factor_cluster == "intrinsic" ~ 0, is.na(dev_factor_cluster) ~
## NA_real_, TRUE ~ NA_real_))
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 572.4 585.0 -283.2 566.4 479
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1498 -0.5462 -0.3689 0.7856 1.8310
##
## Random effects:
## Groups Name Variance Std.Dev.
## ResponseId (Intercept) 1.1262 1.0612
## capacity (Intercept) 0.7454 0.8634
## Number of obs: 482, groups: ResponseId, 288; capacity, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8027 0.6266 -1.281 0.2## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: extrinsic ~ 1 + (1 | ResponseId) + (1 | capacity)
## Data: d3_mech_choice %>% filter(factor == "Social connection") %>%
## mutate(extrinsic = case_when(dev_factor_cluster == "extrinsic" ~
## 1, dev_factor_cluster == "intrinsic" ~ 0, is.na(dev_factor_cluster) ~
## NA_real_, TRUE ~ NA_real_))
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 579.6 592.0 -286.8 573.6 462
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6698 -0.7710 0.3616 0.5989 1.2970
##
## Random effects:
## Groups Name Variance Std.Dev.
## ResponseId (Intercept) 1.6682 1.2916
## capacity (Intercept) 0.8469 0.9203
## Number of obs: 465, groups: ResponseId, 279; capacity, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5217 0.6665 0.783 0.434## boundary (singular) fit: see ?isSingular## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: extrinsic ~ 1 + (1 | ResponseId) + (1 | capacity)
## Data: d3_mech_choice %>% filter(factor == "Cognition and control") %>%
## mutate(extrinsic = case_when(dev_factor_cluster == "extrinsic" ~
## 1, dev_factor_cluster == "intrinsic" ~ 0, is.na(dev_factor_cluster) ~
## NA_real_, TRUE ~ NA_real_))
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 299.9 311.8 -146.9 293.9 386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.05415 0.01476 0.01476 0.01500 0.94864
##
## Random effects:
## Groups Name Variance Std.Dev.
## ResponseId (Intercept) 156.8 12.52
## capacity (Intercept) 0.0 0.00
## Number of obs: 389, groups: ResponseId, 251; capacity, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.3639 0.8337 10.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
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## <99>## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## <99>
Perceived importance of various mechanisms in the development of four domains of mental life (Study 3); see main text for the full text of each mechanism. Panel A shows ratings for each developmental mechanism and both of the capacities within each domain; Panel B shows mean ratings for intrinsic vs. extrinsic mechanisms for each domain of capacities; and Panel C shows the percentage of trials on which participants selected intrinsic vs. extrinsic mechanisms as the ‘most important’ driver of development. Lighter points and lines represent individual participants’ responses, black points correspond to mean scores across the sample, and error bars are bootstrapped 95% confidence intervals. The dotted red line at the midpoint of the response scale in Panels A and B is intended to aid visual comparison across domains.
## Saving 5 x 4.5 in image## Saving 5 x 4.5 in image## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## <98>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <e2>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <80>## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <99>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <e2>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <80>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <98>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <e2>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <80>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <99>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <e2>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <80>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <98>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <e2>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <80>## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## conversion failure on '‘preprogrammed’' in 'mbcsToSbcs': dot substituted for
## <99>